- Understand how Vancouver business’s have evolved in the past
- How they may change in future?
- Can this information be used to drive decision making?
5/8/2020
Goal of the project
Research Question
- How long will a particular Vancouver based business stay in operation?
- Geospatial summary of Vancouver’s business landscape
Dataset Overview
- Licence dataset from 1997 to 2020
- Business type
- Location
- Number of employees
Dataset Overview
- Census local area profile
- Population density
- Average household income
- Average age group
- Ethnicity
Potential future dataset(s)
- Access to public transportation
- Parking space
- Rent per sq./ft
- Construction in the neighbourhood
- Time taken to approve minor renovations
- Registered capital
- Franchise or not
Data Science Technique(s)
- Data wrangling and database creation using Postgres
- Baseline model:
- Logistic regression
- Advance modeling:
- Random forest, Regularization
- Further direction:
- Survival analysis
- Geospatial visualization:
- Python and Altair/Leaflet
- Deploy with Dash on Heroku
Potential difficulties
- Identify and address any existing temporal correlation between the variables
- Current variables may be proxies
- Combine features from different data sources
Final product
- A data pipeline
- A geospatial visualization of Vancouver’s business landscape
Timeline (Week 1 - 2)
Hackathon and Proposal
| Dates | Deliverables | Objectives |
|---|---|---|
| May 4 - 8 | 1. Prepare proposal presentation 2. Setup GitHub repository, prepare data downloading script and data dictionary |
|
| May 8 | Proposal Presentation | |
| May 11 - 15 | 1. Prepare proposal report 2. EDA 3. Finalize form of final product |
|
| May 12 | Proposal Report to Mentor | |
| May 15 | Proposal Report to Deetken |
Timeline (Week 3 - 4)
Launch Project, Develop ML Model, and Build Visualization
| Dates | Deliverables | Objectives |
|---|---|---|
| May 18 - 22 | 1. Incorporate feedbacks from mentor and Deetken on proposal 2. Launch project (both modeling and visualization part) |
|
| May 20 | Meeting with Deetken | |
| May 25 - 29 | Develop machine learning model and geo-spatial visualization |
Timeline (Week 5 - 6)
Fine-tuning
| Dates | Deliverables | Objectives |
|---|---|---|
| Jun 1 - 12 | 1. Fine-tuning of model and visualization 2. Update documentations or user manual for the end product |
Timeline (Week 7 - 9)
Final Stage
| Dates | Deliverables | Objectives |
|---|---|---|
| Jun 15 - 19 | Prepare for final presentation | |
| Jun 18 - 19 | Final Presentation | |
| Jun 22 - 26 | Prepare final report and end data product | |
| Jun 23 | Final report and Product to Mentor | Modify based on feedbacks from mentor |
| Jun 29 | Final Report and Product to Deetken | Final presentation to Deetken |
| Jun 30 | Teamwork Reflection |
Thank you!
Questions?